Dask bags are another way to work with large datasets in a parallelized manner. Similar to Dask DataFrames, Dask bags allow you to break up your data into chunks that can be processed in parallel across multiple cores or machines. However, unlike Dask DataFrames, Dask bags are designed to work with non-tabular data, such as text or JSON files.
To create a Dask bag, you can use the dask.bag.from_files function and pass it a globstring that matches the files you want to include. For example, if you have a directory containing multiple text files, you can create a Dask bag like this:
import dask.bag as dbbag = db.from_filenames('/path/to/text/files/*.txt') |
This will create a Dask bag that contains all of the text files in the specified directory. Each file will be treated as a separate partition, and Dask will process them in parallel.
Once you have created a Dask bag, you can perform various operations on it, such as filtering, mapping, and reducing. For example, you can use the filter method to select only the lines in the text files that contain a certain string:
filtered_bag = bag.filter(lambda line: 'target_string' in line) |
This will create a new Dask bag that contains only the lines that match the specified filter. You can also use the map method to apply a function to each element in the bag, and the reduce method to aggregate the elements in the bag using a given function.
Overall, Dask bags provide a flexible and efficient way to work with non-tabular data in a parallelized manner. They can be especially useful when dealing with large text or JSON files that cannot be easily loaded into memory.